The main assignment here is to expand the estimation problem to the multiparameter case by estimating a set of unknown parameters. In this case, the various parameters we wish to estimate are constructed in the vector defined in the parameter space \( \theta \left(x \right)=\left(\theta _{1}\left( x\right),...,\theta _{d}\left(x\right)\right)^{T}\in \mathbb {R}^{d}\) . Thereby, the realization of the estimation vector is represented by \(\hat{ \theta }\left( x\right)=\left(\hat{\theta }_{1}\left(x \right),...,\hat{\theta }_{d}\left(x\right)\right)^{T}\) . For any given estimator \(\hat{ \theta }\left(x\right)\)  of \(\theta \left(x\right)\) , the mathematical expectation is given by \(\textbf {E}\left[\left(\theta - \hat{\theta }\right) \left(\theta - \hat{\theta }\right)^{T}\right]\) . Subsequently, the mean square error is equal to the covariance matrix \({\rm Cov}\left( \hat{ \theta }\right)\) . One of the key results of classical probability theory, namely the Cramér-Rao inequality, enforcing a lower bound on the covariance matrix [1]}
\({\rm Cov}\left( \hat{ \theta }\right)\ge \mathbb {F}\left( \theta  \right)^{-1},\) 
